http://iet.metastore.ingenta.com
1887

A comparative evaluation on linear and nonlinear despeckle filtering techniques

A comparative evaluation on linear and nonlinear despeckle filtering techniques

For access to this article, please select a purchase option:

Buy chapter PDF
£10.00
(plus tax if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Handbook of Speckle Filtering and Tracking in Cardiovascular Ultrasound Imaging and Video — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In this chapter, the methods of texture analysis, image quality evaluation, distance measures, univariate statistical analysis and the k-nearest-neighbor (kNN) classifier, which are used to evaluate despeckle filtering on ultrasound imaging and video, are presented. For speckle reduction, 16 different despeckle filtering methods, already described in [1,2], were applied to each image or video prior to intima-media complex (IMC) or atherosclerotic plaque segmentation. Despeckle filtering was applied after image or video normalization , either to the entire image or to an ROI, selected by the user. The selected area of interest (ROI) can be of any shape but the image despeckle filtering (IDF) software doesn't support multiple ROIs selection. In the latter case, where the user of the system is interested only in the selected ROI, the area outside the ROI can be blurred using the DsFlsmv filter operating with a sliding moving window of [15x15] pixels and a number iterations 5. It should be noted that the blurring is applied outside of the ROI if the user of the system is not interested to subjectively evaluate this area. The input parameters of the 16 different despeckle filters for the IDF and video despeckle filtering (VDF) software toolboxes can be selected by the user as it was documented in [1-4]. The 16 despeckle filters evaluated in this chapter were applied on a large number of asymptomatic (AS) and of symptomatic (SY) ultrasound images (220 vs 220) of the common carotid artery (CCA). Four despeckle filters (DsFlsmv, DsFhmedian, DsFkuwahara, DsFsrad) were further applied to ten videos of the carotid artery bifurcation. A large number of texture features (61 different texture features) were extracted from the original and despeckle images and videos, and the most discriminant ones are presented. The performance of these filters is investigated for discriminating between AS and SY images using the statistical kNN classifier. Moreover, 16 different image quality evaluation metrics were computed, as well as visual evaluation scores carried out by two experts.

Chapter Contents:

  • 10.1 Despeckle filtering evaluation of carotid plaque imaging based on texture analysis
  • 10.1.1 Distance measures
  • 10.1.2 Univariate statistical analysis
  • 10.1.3 The kNN classifier
  • 10.1.4 Image and video quality and visual evaluation
  • 10.2 Despeckle filtering based on texture analysis (discussion)
  • 10.3 Image despeckle filtering based on visual quality evaluation (discussion)
  • 10.4 Despeckle filtering evaluation on carotid plaque video based on texture analysis
  • 10.5 Video despeckle filtering based on texture analysis and visual quality evaluation (discussion)
  • 10.6 Concluding remarks and future directions
  • References

Inspec keywords: image texture; video signal processing; nonlinear filters; medical image processing; statistical analysis; blood vessels; biomedical ultrasonics; feature extraction; image classification; software tools; image filtering

Other keywords: nonlinear despeckle filtering techniques; univariate statistical analysis; speckle reduction; common carotid artery; DsFlsmv filter; atherosclerotic plaque segmentation; statistical kNN classifier; symptomatic ultrasound images; texture feature extraction; image quality evaluation metrics; video normalization; intima-media complex; linear despeckle filtering techniques; video despeckle filtering software toolboxes; carotid artery bifurcation; asymptomatic ultrasound images; image despeckle filtering software; visual evaluation scores; k-nearest-neighbor classifier; texture analysis; image normalization

Subjects: Other topics in statistics; Sonic and ultrasonic radiation (biomedical imaging/measurement); Image recognition; Computer vision and image processing techniques; Biology and medical computing; Video signal processing; Sonic and ultrasonic applications; Sonic and ultrasonic radiation (medical uses); Probability theory, stochastic processes, and statistics; Other topics in statistics; Patient diagnostic methods and instrumentation

Preview this chapter:
Zoom in
Zoomout

A comparative evaluation on linear and nonlinear despeckle filtering techniques, Page 1 of 2

| /docserver/preview/fulltext/books/he/pbhe013e/PBHE013E_ch10-1.gif /docserver/preview/fulltext/books/he/pbhe013e/PBHE013E_ch10-2.gif

Related content

content/books/10.1049/pbhe013e_ch10
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address